Graph-based prediction for contact suggestion in a location sharing system

ABSTRACT

Methods, systems, and devices for generating contact suggestions for a user of a social network. A first score is computed for each one of the plurality of users, the first score being computed using an edge-weighted ranking algorithm based on the user graph. A second score is computed, using a machine learning model, for each user of the plurality of users, the second score of each user being, at least partially, based on the first score of said user, with the second score of each user being representative of a probability of a first user sending a connection request to said user. A ranked contact suggestion list of one or more users of the plurality of users is generated, the one or more users being ranked based on their respective second score.

BACKGROUND

The popularity of electronic messaging, particularly instant messaging, continues to grow. Users increasingly share media content items such as electronic images and videos with each other, reflecting a global demand to communicate more visually. Similarly, users increasingly seek to customize the media content items they share with others, providing challenges to social networking systems seeking to generate custom media content for their members. Embodiments of the present disclosure address these and other issues.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced.

FIG. 1 is a diagrammatic representation of a networked environment in which the present disclosure may be deployed, in accordance with some example embodiments.

FIG. 2 is a diagrammatic representation of a data structure as maintained in a database, in accordance with some example embodiments.

FIG. 3 is a diagrammatic representation of a processing environment, in accordance with some example embodiments.

FIG. 4 is block diagram showing a software architecture within which the present disclosure may be implemented, in accordance with some example embodiments.

FIG. 5 is a diagrammatic representation of a machine, in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed, in accordance with some example embodiments.

FIG. 6 illustrates a method in accordance with one embodiment.

FIG. 7 illustrates a method in accordance with one embodiment.

FIG. 8 illustrates a method in accordance with one embodiment.

FIG. 9 illustrates a method in accordance with one embodiment.

FIG. 10 illustrates a model in accordance with one embodiment.

FIG. 11 illustrates a user interface in accordance with one embodiment.

DETAILED DESCRIPTION

Various embodiments of the present disclosure provide systems, methods, techniques, instruction sequences, and computing machine program products for generating contact suggestions for a user of a social network.

Conventional methods for generating contact suggestions for a user of a social network are limited in their ability to generate relevant contact suggestions. Motivated by these challenges, some embodiments of the present disclosure provide improvements over conventional methods for generating contact suggestions by building a user graph and generating contact suggestions based on the user graph. In some embodiments, the contact suggestions are then ranked using a machine learning model trained to predict a probability of a user sending a connection request to another user based on being presented with the other user as a contact suggestion.

For example, in some embodiments, user data of a plurality of users are accessed. A user graph of the plurality of users is generated. In the user graph, two users that have a relationship (e.g., “friends” in the social network graph) are connected via a weighted edge. The weight of the weighted edge between the two users is based, at least partially, on an amount of time the two users that have a relationship (e.g., “friends” in the social network graph) have spent together. A first score is computed for each one of the plurality of users, the first score being computed using an edge-weighted, edge-based ranking algorithm based on the user graph. A second score is computed, using a machine learning model, for each user of the plurality of users, the second score of each user being, at least partially, based on the first score of said user, the second score of each user being representative of a probability of a first user sending a connection request to said user. A ranked contact suggestion list of one or more users of the plurality of users is generated, with the one or more users being ranked based on their respective second score. The ranked list is displayed on a client device of a first user.

The present disclosure provides various improvements over conventional user interfaces. In particular, in some embodiments, the amount of time users spend with the users they are connected to (e.g., “friends” in a social network graph) is tracked, and the contact suggestion list displayed on the graphical user interface (GUI) is updated based on the amount of time the users have been spending together, automatically moving the contact suggestions with which the first user is most likely to interact to a higher position on the GUI based on the determined amount of time the users have been spending together.

The description that follows includes systems, methods, techniques, instruction sequences, and computing machine program products that embody illustrative embodiments of the disclosure. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide an understanding of various embodiments of the inventive subject matter. It will be evident, however, to those skilled in the art, that embodiments of the inventive subject matter may be practiced without these specific details. In general, well-known instruction instances, protocols, structures, and techniques are not necessarily shown in detail.

FIG. 1 is a block diagram showing an example location sharing system 100 for exchanging location data over a network. The location sharing system 100 includes multiple instances of a client device 102, each of which hosts a number of applications including a location sharing client application 104. Each location sharing client application 104 is communicatively coupled to other instances of the location sharing client application 104 and a location sharing server system 108 via a network 106 (e.g., the Internet).

A location sharing client application 104 is able to communicate and exchange data with another location sharing client application 104 and with the location sharing server system 108 via the network 106. The data exchanged between location sharing client application 104, and between a location sharing client application 104 and the location sharing server system 108, includes functions (e.g., commands to invoke functions) and payload data (e.g., location data, text, audio, video or other multimedia data).

The location sharing server system 108 provides server-side functionality via the network 106 to a particular location sharing client application 104. While certain functions of the location sharing system 100 are described herein as being performed by either a location sharing client application 104 or by the location sharing server system 108, the location of certain functionality either within the location sharing client application 104 or the location sharing server system 108 is a design choice. For example, it may be technically preferable to initially deploy certain technology and functionality within the location sharing server system 108 but to later migrate this technology and functionality to the location sharing client application 104 where a client device 102 has a sufficient processing capacity.

The location sharing server system 108 supports various services and operations that are provided to the location sharing client application 104. Such operations include transmitting data to, receiving data from, and processing data generated by the location sharing client application 104. This data may include geolocation information, message content, client device information, media annotation and overlays, message content persistence conditions, social network information, and live event information, as examples. Data exchanges within the location sharing system 100 are invoked and controlled through functions available via user interfaces (UIs) of the location sharing client application 104.

Turning now specifically to the location sharing server system 108, an Application Program Interface (API) server 110 is coupled to, and provides a programmatic interface to, an application server 112. The application server 112 is communicatively coupled to a database server 118, which facilitates access to a database 120 in which is stored data associated with messages processed by the application server 112.

The API server 110 receives and transmits message data (e.g., commands and message payloads) between the client device 102 and the application server 112. Specifically, the API server 110 provides a set of interfaces (e.g., routines and protocols) that can be called or queried by the location sharing client application 104 in order to invoke functionality of the application server 112. The API server 110 exposes various functions supported by the application server 112, including account registration; login functionality; the sending of messages, via the application server 112, from a particular location sharing client application 104 to another location sharing client application 104; the sending of media files (e.g., images or video) from a location sharing client application 104 to the location sharing server application 114 and for possible access by another location sharing client application 104; the setting of a collection of media data (e.g., story); the retrieval of a list of friends of a user of a client device 102; the retrieval of such collections; the retrieval of messages and content; the addition and deletion of friends to a social graph; the location of friends within a social graph; and opening an application event (e.g., relating to the location sharing client application 104).

The application server 112 hosts a number of applications and subsystems, including a location sharing server application 114, a messaging server application 116, and a social network system 122.

Examples of functions and services supported by the location sharing server application 114 include generating a map GUI. In some embodiments, the map GUI may include representations of at least approximate respective positions of a user and a user's friends in a social network graph accessed by the social media application using avatars for each respective user.

The location sharing server application 114 may receive user authorization to use, or refrain from using, the user's location information. In some embodiments, the location sharing server application 114 may likewise opt to share or not share the user's location with others via the map GUI. In some cases, the user's avatar may be displayed to the user on the display screen of the user's computing device regardless of whether the user is sharing his or her location with other users.

In some embodiments, a user can select groups of other users (audiences) to which his/her location will be displayed and may specify different display attributes for the different respective groups or for different respective individuals. In one example, audience options include: “Best Friends,” “Friends,” and “Custom” (which is an individual-level whitelist of people). In this example, if “Friends” is selected, all new people added to the user's friends list will automatically be able to see their location. If they are already sharing with the user, their avatars will appear on the user's map.

In some embodiments, when viewing the map GUI, the user is able to see the location of all his/her friends that have shared their location with the user on the map, with each friend represented by their respective avatar. In some embodiments, if a friend does not have an avatar, the friend may be represented using a profile picture or a default icon displayed at the corresponding location for the friend.

In some embodiments, the user can select between friends on the map via a menu, such as a carousel. In some embodiments, selecting a particular friend automatically centers the map view on the avatar of that friend. Embodiments of the present disclosure may also allow the user to take a variety of actions with the user's friends from within the map GUI. For example, the system may allow the user to chat with the user's friends without leaving the map. In one particular example, the user may select a chat icon from a menu presented in conjunction with the map GUI to initiate a chat session.

The client device 102 host a messaging client application 124. The messaging server application 116 implements a number of message processing technologies and functions, particularly related to the aggregation and other processing of content (e.g., textual and multimedia content) included in messages received from multiple instances of the location sharing client application 104. As will be described in further detail, the text and media content from multiple sources may be aggregated into collections of content (e.g., called stories or galleries). These collections are then made available, by the location sharing server application 114, to the location sharing client application 104. Other processor and memory intensive processing of data may also be performed server-side by the location sharing server application 114, in view of the hardware requirements for such processing.

The application server 112 is communicatively coupled to a database server 118, which facilitates access to a database 120 in which is stored data processed by the location sharing server application 114.

The social network system 122 supports various social networking functions and services, and makes these functions and services available to the location sharing server application 114. To this end, the social network system 122 maintains and accesses an entity graph 30 a user graph 204 (as shown in FIG. 2 ) within the database 120. Examples of functions and services supported by the social network system 122 include the identification of other users of the location sharing system 100 with which a particular user has relationships or is “following” and also the identification of other entities and interests of a particular user.

FIG. 2 is a schematic diagram illustrating data structures 200 which may be stored in the database 120 of the location sharing server system 108, according to certain example embodiments. While the content of the database 120 is shown to comprise a number of tables, it will be appreciated that the data could be stored in other types of data structures (e.g., as an object-oriented database).

The database 120 includes message data stored within a message table 210. A user table 202 stores user data, including a user graph 204. The user graph 204 furthermore stores information regarding relationships and associations between users. Such relationships may be social, professional (e.g., work at a common corporation or organization) interested-based or activity-based, merely for example. Such relationships may include that a user is sharing his/her location with another user via the location sharing system (e.g., location sharing system 100). Such relationships may include an amount of time (e.g., tracked by the location sharing system 100) a user has spent with another user. Such relationships may include that a user is a preferred contact (e.g., “friend” in the social network graph) of another user in a social network (e.g., social network system 122). Such relationships may include that a user is in an address book of another user in a messaging application (e.g., messaging client application 124). Such relationships may include that a user is in a contact book stored in a client device (e.g., client device 102) of another user. The user table 202 may also store the amount of time a user has spent with each of the users with whom the given user has a relationship.

A location table 206 stores historical and current location data of users (e.g., geolocation information determined by a positioning system (e.g., GPS) unit of client devices (e.g., client device 102).

A training data table 208 stores training data comprising historical contact suggestions provided to users, and connection requests sent. In some embodiments, the training data further comprises resulting connections created based on users accepting a connection request. The connection request may be a request to connect via the location sharing system 100, and/or the messaging application 446, and/or the social network system 122.

Turning now to FIG. 3 , there is shown a diagrammatic representation of a processing environment 300, which includes at least a processor 302 (e.g., a GPU, CPU or combination thereof).

The processor 302 is shown to be coupled to a power source 304, and to include (either permanently configured or temporarily instantiated) modules, namely a location component 306, a link analysis component 308, a time component 310, a graph component 318, a machine learning component 314, a training component 316, and a UI component 312. The location component 306 determines a location of a user based on location data of the user collected by one or more client device (e.g., client device 102) associated with the user. The time component 310 accesses location data from the location component 306, and tracks, for a given user, the amount of time the given user has spent with the users with whom the given user has a relationship. In some embodiments, two users are considered to spend time together when their respective location is within a preset distance of each other. The graph component 318 accesses user data from the user table 202 and builds a user graph 204 based on the user data. The link analysis component 308 performs a link analysis algorithm on the graph to generate a ranked list of contact suggestions. The training component 316 accesses the training data and trains the machine learning component 314 on the task of predicting the probability of a user sending a connection request to another user. The machine learning component 314 accesses the ranked list of contact suggestions outputted by the link analysis component and outputs a reranked contact suggestion lists, ranked based on the probability of the user sending a connection request to each user of the reranked contact suggestion list. The UI component 312 operationally generates user interfaces comprising the reranked list of contact suggestion and causes the user interfaces to be displayed on client devices.

FIG. 4 is a block diagram 400 illustrating a software architecture 404, which can be installed on any one or more of the devices described herein. The software architecture 404 is supported by hardware such as a machine 402 that includes processors 420, memory 426, and I/O components 438. In this example, the software architecture 404 can be conceptualized as a stack of layers, where each layer provides a particular functionality. The software architecture 404 includes layers such as an operating system 412, libraries 410, frameworks 408, and applications 406. Operationally, the applications 406 invoke API calls 450 through the software stack and receive messages 452 in response to the API calls 450.

The operating system 412 manages hardware resources and provides common services. The operating system 412 includes, for example, a kernel 414, services 416, and drivers 422. The kernel 414 acts as an abstraction layer between the hardware and the other software layers. For example, the kernel 414 provides memory management, processor management (e.g., scheduling), component management, networking, and security settings, among other functionality. The services 416 can provide other common services for the other software layers. The drivers 422 are responsible for controlling or interfacing with the underlying hardware. For instance, the drivers 422 can include display drivers, camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) drivers), WI-FI® drivers, audio drivers, power management drivers, and so forth.

The libraries 410 provide a low-level common infrastructure used by the applications 406. The libraries 410 can include system libraries 418 (e.g., C standard library) that provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries 410 can include API libraries 424 such as media libraries (e.g., libraries to support presentation and manipulation of various media formats such as Moving Picture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC), Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group (JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries (e.g., an OpenGL framework used to render in two dimensions (2D) and three dimensions (3D) in a graphic content on a display), database libraries (e.g., SQLite to provide various relational database functions), web libraries (e.g., WebKit to provide web browsing functionality), and the like. The libraries 410 can also include a wide variety of other libraries 428 to provide many other APIs to the applications 406.

The frameworks 408 provide a high-level common infrastructure that is used by the applications 406. For example, the frameworks 408 provide various graphical user interface (GUI) functions, high-level resource management, and high-level location services. The frameworks 408 can provide a broad spectrum of other APIs that can be used by the applications 406, some of which may be specific to a particular operating system or platform.

In an example embodiment, the applications 406 may include a home application 436, a contacts application 430, a browser application 432, a book reader application 434, a location application 442, a media application 444, a messaging application 446, a game application 448, and a broad assortment of other applications such as third-party applications 440. The applications 406 are programs that execute functions defined in the programs. Various programming languages can be employed to create one or more of the applications 406, structured in a variety of manners, such as object-oriented programming languages (e.g., Objective-C, Java, or C++) or procedural programming languages (e.g., C or assembly language). In a specific example, the third-party applications 440 (e.g., applications developed using the ANDROID™ or IOS™ software development kit (SDK) by an entity other than the vendor of the particular platform) may be mobile software running on a mobile operating system such as IOS™, ANDROID™, WINDOWS® Phone, or another mobile operating system. In this example, the third-party applications 440 can invoke the API calls 450 provided by the operating system 412 to facilitate functionality described herein.

FIG. 5 is a diagrammatic representation of a machine 500 within which instructions 508 (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine 500 to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions 508 may cause the machine 500 to execute any one or more of the methods described herein. The instructions 508 transform the general, non-programmed machine 500 into a particular machine 500 programmed to carry out the described and illustrated functions in the manner described. The machine 500 may operate as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine 500 may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine 500 may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a PDA, an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions 508, sequentially or otherwise, that specify actions to be taken by the machine 500. Further, while only a single machine 500 is illustrated, the term “machine” shall also be taken to include a collection of machines that individually or jointly execute the instructions 508 to perform any one or more of the methodologies discussed herein.

The machine 500 may include processors 502, memory 504, and I/O components 542, which may be configured to communicate with each other via a bus 544. In an example embodiment, the processors 502 (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an ASIC, a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor 506 and a processor 510 that execute the instructions 508. The term “processor” is intended to include multi-core processors that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. Although FIG. 5 shows multiple processors 502, the machine 500 may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof.

The memory 504 includes a main memory 512, a static memory 514, and a storage unit 516, both accessible to the processors 502 via the bus 544. The main memory 504, the static memory 514, and storage unit 516 store the instructions 508 embodying any one or more of the methodologies or functions described herein. The instructions 508 may also reside, completely or partially, within the main memory 512, within the static memory 514, within machine-readable medium 518 within the storage unit 516, within at least one of the processors 502 (e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine 500.

The I/O components 542 may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components 542 that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones may include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components 542 may include many other components that are not shown in FIG. 5 . In various example embodiments, the I/O components 542 may include output components 528 and input components 530. The output components 528 may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The input components 530 may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or another pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like.

In further example embodiments, the I/O components 542 may include biometric components 532, motion components 534, environmental components 536, or position components 538, among a wide array of other components. For example, the biometric components 532 include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram-based identification), and the like. The motion components 534 include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environmental components 536 include, for example, illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas detection sensors to detection concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment. The position components 538 include location sensor components (e.g., a GPS receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies. The I/O components 542 further include communication components 540 operable to couple the machine 500 to a network 520 or devices 522 via a coupling 524 and a coupling 526, respectively. For example, the communication components 540 may include a network interface component or another suitable device to interface with the network 520. In further examples, the communication components 540 may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices 522 may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 540 may detect identifiers or include components operable to detect identifiers. For example, the communication components 540 may include Radio Frequency Identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components 540, such as location via Internet Protocol (IP) geolocation, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth.

The various memories (e.g., memory 504, main memory 512, static memory 514, and/or memory of the processors 502) and/or storage unit 516 may store one or more sets of instructions and data structures (e.g., software) embodying or used by any one or more of the methodologies or functions described herein. These instructions (e.g., the instructions 508), when executed by processors 502, cause various operations to implement the disclosed embodiments.

The instructions 508 may be transmitted or received over the network 520, using a transmission medium, via a network interface device (e.g., a network interface component included in the communication components 540) and using any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions 508 may be transmitted or received using a transmission medium via the coupling 526 (e.g., a peer-to-peer coupling) to the devices 522.

FIG. 6 is a flowchart illustrating a method 600 for generating a contact suggestion list for a first user and displaying the contact suggestion list to the first user. The method 600 may be embodied in computer-readable instructions for execution by one or more processors (e.g., processor 302) such that the steps of the method 600 may be performed in part or in whole by functional components (e.g., location component 308, link analysis component 308, a time component 312, a graph component 320, a machine learning component 316, training component 318, and UI component 314) of a processing environment 300 of a system (e.g., application server 112); accordingly, the method 600 is described below by way of example with reference thereto. However, it shall be appreciated that the method 600 may be deployed on various other hardware configurations and is not intended to be limited to the functional components of the processing environment 300.

In block 602, method 600 accesses, from a database (e.g., database 120) coupled to a server computer (e.g., application server 112), user data of a plurality of users.

The system may need to receive authorization from the user to utilize user data prior to performing the remaining steps of method 600. Such authorization may be obtained via acceptance of a terms of service for utilizing an online social network or other service provided by the system, by acceptance on a case-by-case basis by the user (e.g., via popups displayed on the user's computing device), or using any other suitable method for obtaining authorization by the user(s).

The plurality of users is selected among all the users registered in a user table (e.g., user table 202) based on their relationship with the first user. The plurality of users may be users of a location tracking system, a social media application, and/or a messaging system. In some embodiments, the plurality of users are the users having at least either a one-degree incoming relationship to the first user or a two-degree outgoing relationship from the first user.

A degree of a relationship between user u and user v is a number of links between user u and user v. If user u is in a direct relationship with user v, user u and user v are in a one-degree relationship. If user u is in a direct relationship with user w, and user w is in a direct relationship with user v, the user u and user v are in a two-degree relationship.

A user u has an incoming relationship from user v when there is a relationship from user v to user u, such as, for example, user v is sharing his/her location with user u via the location sharing system (e.g., location sharing system 100), user u is a preferred contact (e.g., “friend” in the social network graph) of user v in a social network (e.g., social network system 122), user u is in an address book of user v in a messaging application (e.g., messaging application 446), or user u is in an address book stored in a client device (e.g., client device 102) of user v.

A user u has an outgoing relationship to user v when there is a relationship from user u to user v, such as, for example, user u is sharing his/her location with user v via the location sharing system (e.g., location sharing system 100), user u is a preferred contact (e.g., “friend” in the social network graph) of user v in a social network (e.g., social network system 122), user u is in an address book of user v in a messaging application (e.g., messaging application 446), or user v is in an address book stored in a client device (e.g., client device 102) of user u.

In block 604, method 600 generates, using one or more processors of the server computer, a user graph (e.g., user graph 1002 of FIG. 10 ) of the plurality of users. The graph component 318 accesses the user table 202 and builds the weighted graph based on the information retrieved from the user table 202. The graph includes an edge between every pair of users for which a relationship is recorded in the user table 202. The graph may be a directed graph.

The graph may be a weighted graph. The weight of the weighted edge between a user u and a user v may be based on one or more of the following criteria:

-   -   for every pair of users (u, v), the weight of the weighted edge         from user u to user v is incremented if user v is included in a         group of users with which user u is sharing his/her position         (e.g., via the location sharing system 100);     -   for every pair of users (u, v), the weight of the weighted edge         from user u to user v is incremented if user v is included in a         group of user u, such as “Best Friends,” “Friends,” and “Custom”         (which is an individual-level whitelist of people) in a social         network (e.g., in the social network system 122);     -   for every pair of users (u, v), the weight of the weighted edge         from user u to user v is incremented if user v is a preferred         contact (e.g., “friend” in the social network graph) of user u         in a social network (e.g., social network system 122);     -   for every pair of users (u, v), the weight of the weighted edge         from user u to user v is incremented if user v is in an address         book of user u in a messaging application (e.g., messaging         application 446);     -   for every pair of users (u, v), the weight of the weighted edge         from user u to user v is incremented if user v is in an address         book stored in a client device of user u;     -   for every pair of users (u, v), the weight of the weighted edge         from user u to user v is based on whether user u sent a         connection request to user v. Such connection request may be an         invitation to share his/her position via the location sharing         system 100;     -   for every pair of users (u, v) that have a relationship (e.g.,         “friends” in the social network graph), a weight of a weighted         edge between user u and user v is based, at least partially, on         an amount of time user u and user v have spent together.

In block 606, method 600 (e.g., the link analysis component 308) computes, by the one or more processors, a first score for each one of the plurality of users, based on the graph. The first score is computed using a link-based ranking algorithm (e.g., edge-weighted PageRank) based on the user graph (e.g., user graph 1002 of FIG. 10 ) of the plurality of users. In embodiments, if the graph is a weighted graph, the first score is computed using an edge-weighted edge-based ranking algorithm (e.g., edge-weighted PageRank) based on the weighted user graph.

The algorithm may be initialized to a same value for all users. At each iteration, a contribution to the value PR(u) of a user u conferred by an edge from user v to user u is equal to the value PR (v) of user v divided by the number of edges L(v) from user v weighted by the weight of the edge W(u, v) from user v to user u.

In embodiments, the value for any user can be expressed as:

${P{R(u)}} = {\sum\limits_{v \in B_{u}}{{W\left( {u,v} \right)}\frac{P{R(v)}}{L(v)}}}$ The value PR (u) for a user u is dependent on the values PR (v) for each user v contained in the set B u (the set containing all users linked to user u), divided by the number L(v) of links from the user v, and weighted by the weight W(u, v) of the link from user v to user u in case of a directed graph.

In embodiments, the link analysis component 308 generates a ranked list of the users (e.g., ranked list 1004 of FIG. 10 ), with the users being ranked based on their respective first score.

In block 608, method 600 (e.g., the machine learning component 314) computes a second score for each user of the plurality of users. The second score of a user may be computed as a probability of the first user sending a connection request to the user upon being presented with a suggestion to add the user as a contact. The second score of a user may also be computed as a probability of the user accepting a connection request sent by the first user. As explained in more details in relation to FIG. 7 , the second score of a user may be computed using a machine learning model (e.g., machine learning model 1006 of FIG. 10 ).

The second score may depend on the age of the first user. For example, the score may be higher for a younger first user and lower for an older first user. Indeed, it has been observed that younger users are more likely to send a connection request to users with whom they have a weaker connection, while older users are more likely to only send a connection request to users with whom they have a stronger connection. The score may also depend on an age difference between the first user and the user. The score may also depend on a distance between the location of the first user and the location of the user at computation time. The score may also depend on the country of residency of the first user.

In block 610, method 600 (e.g., the machine learning component 316) generates a contact suggestion list (e.g., reranked list 1008 of FIG. 10 ) of one or more of the plurality of users, the contact suggestion list being ranked based on the second score of the plurality of users. In embodiments, the users having a higher second score are ranked higher in the contact suggestion list.

In block 612, method 600 (e.g., the UI component 314) causes display, on a client device of the first user, of the contact suggestion list (e.g., user interface 1100 of FIG. 11 ).

As shown in FIG. 7 , the method 600 may further comprise a block 702 and a block 704, according to some embodiments. Consistent with some embodiments, block 702 may be performed before block 602, and block 704 may be performed as part of (e.g., as sub-blocks or as a subroutine) of block 608, where the system computes a second score for each of the users of the plurality of users.

In block 702, the method 600 (e.g., the training component 318) trains a machine learning model (e.g., machine learning model 1006) on a training set comprising historical user data. The machine learning model may include an artificial neural network or a random forest classifier. The machine learning model may include an artificial neural network, a random forest classifier, or gradient boosted trees. Gradient-boosted trees are based on an ensemble of tree-based classifiers that are iteratively optimized to minimize the prediction error.” In some embodiments, the historical user data comprises historical contact suggestions and resulting connection requests (e.g., contact suggestion presented to a user u to add a user v as a contact and a resulting connection request sent from user u to user v). The connection request may be a request to connect via the location sharing system 100, and/or the messaging application 446, and/or the social network system 122. The machine learning model is trained on a learning task defined as predicting a probability of a user sending a connection request to another user.

In some embodiments, the historical data further comprises connections created as a result of contact suggestions presented to users (e.g., connection created from user u to user v, as a result of user v accepting the connection request sent by user u). The connection may be a connection via the location sharing system 100, and/or the messaging application 446, and/or the social network system 122. The machine learning model is trained on a learning task defined as predicting a probability of a user accepting a connection request received form another user.

In block 704, method 600 (e.g., the machine learning component 316) computes the second score using the trained machine learning model (e.g., machine learning model 1006). In some embodiments, the trained machine learning model computes the second score of each user as a probability of the first user sending a connection request to said user. In some embodiments, the trained machine learning model computes the second score of each user as a probability of said user accepting a connection request from the first user. In block 608, method 600 (e.g., the machine learning component 314) computes the second score of each user, based, at least partially, on the second score of said user.

As shown in FIG. 8 , the method 600 may further comprise a block 802, according to some embodiments. Consistent with some embodiments, block 802 may be performed as part of (e.g., as sub-blocks or as a subroutine) of block 610, where the system generates a contact suggestion list including one or more of the plurality of users.

In block 802, the method 600 filters out the users having a second score below a threshold. The threshold is selected as a compromise between the number of contact suggestion and the relevance of the contact suggestions.

In block 610, the contact suggestion list displayed on the display screen of the client device of the first user only includes users that have a second score that exceeds the threshold.

As shown in FIG. 9 , the method 600 may further comprise a block 902, a block 904, a block 906, a block 908, a block 910, a block 912, a block 914, block 916 and a block 918 according to some embodiments. Consistent with some embodiments, block 902, block 904, block 906, block 908, block 910, block 912, block 914, and block 916 may be performed as part of (e.g., as sub-blocks or as a subroutine) of block 612, where the system causes display of the ranked contact suggestion list on the client device of the first user.

In block 902, the method 600 receives new location data of one or more users of the plurality of users. In some embodiments, the location sharing server application 114 receives, from one or more client devices (e.g., client device 102) associated with one or more of the plurality of users, via a wireless communication, over a network (e.g., network 106), an electronic communication containing current location data of the user. The current location data of the user may include location data gathered by one or more of the client devices of the user over a recent period of time. The system may receive location data on a periodic basis or on an irregular basis and may request data from the client device or receive such data from the client device without such a request. In some embodiments, the client device contains software that monitors the location from the client device and transmits updates to the system in response to detecting new location. For example, the client device may update the system with a new location only after the location changes by at least a predetermined distance to allow a user to move about a building or other location without triggering updates.

In block 904, method 600 (e.g., the time component 312) detects, based on the new location data, that at least two users connected via an edge of the user graph (e.g., users that are “friends” in the social network graph) have been spending time together. In embodiments, users are considered to spend time together when they are located within a maximum distance of each other for a minimum amount of time. For example, the time component 312 detects, based on location data received from user u and user v, that user u and user v have been spending time together since the last time the amount of time user u and user v had spent together was last computed.

In block 906, method 600 (e.g., the time component 312) computes, based on detecting that user u and user v have been spending time together since the last time the amount of time user u and user v have spent together was computed, an updated amount of time user u and user v have spent together and updates the user table 202 with the updated amount of time.

In block 908, method 600 (e.g., the graph component 320) computes, based on the updated amount of time user u and user v have spent together, an updated weight for the weighted edge between the user u and user v.

In block 910, method 600 (e.g., the graph component 320) generates, based on the updated weight, an updated user graph of the plurality of users.

In block 912, method 600 (e.g., the link analysis component 308) computes, based on the updated user graph, an updated first score for each one of the plurality of users.

In block 914, method 600 (e.g., the machine learning component 316) computes, based on the updated first score of each of the plurality of users, an updated second score for each user of the plurality of users.

In block 916, method 600 (e.g., the contact suggestion component 316) generates an updated ranked contact suggestion list based on the updated second score of each user of the plurality of users.

In block 918, method 600 (e.g., the UI component 314) automatically causes display, on the client device of the first user, of the updated ranked contact suggestion list. In embodiments, the UI component 314 reorders the icons displayed on the user interface based on the updated second score of each of the one or more users.

FIG. 10 illustrates a model 1000 implemented in some embodiments of method 600. The model 1000 includes a user graph 1002 of the plurality of users (e.g., implemented by the graph component 320) and a machine learning model 1006 (e.g., implemented by the machine learning component 316). The user graph 1002 is built based on user data related to a first user (user u-1). A link analysis algorithm is performed on the user graph 1002 (e.g., by the link analysis component 308) and outputs a ranked list 1004 of one or more users of the plurality of users. The ranked list 1004 is processed through the machine learning model 1006. The machine learning model 1006 outputs a reranked list 1008 comprising the one or more users of the ranked list 1004 ranked according to their second score, with the users having a higher second score being ranked higher in the reranked list 1008.

As shown in FIG. 11 , user interface 1100 is an example of a user interface that may be displayed on a display screen of a client device of a first user. The user interface 1100 comprises a ranked contact suggestion list comprising a plurality of contact suggestions 1102, with each contact suggestion 1102 being associated with a user included in the ranked contact suggestion list. Each contact suggestion 1102 is displayed alongside an interactive user interface element 1104, with the interactive user interface element 1104 triggering the emission of a connection request from the first user to the user associated with the contact suggestion 1102.

Upon detecting a user interaction with one of the interactive user interface elements 1104, a connection request is sent to the user associated with the contact suggestion 1102. If the user associated with the contact suggestion 1102 accepts the connection request, a relationship between the first user and the user is created. The relationship is registered in the user table 202. In embodiments, the user graph 204 is updated based on the updated user table 202, and the ranked contact suggestion list displayed on the user interface 1100 is updated based on the updated user graph 204, automatically moving the contact suggestions with whom the first user is most likely to interact with to a higher position on the user interface 1100 based on the newly created relationship.

In embodiments, the amount of time users that have a relationship (e.g., “friends” in the social network graph) spend together is tracked, and the ranked contact suggestion list displayed on the graphical user interface 1100 is updated based on the amount of time the users that have a relationship (e.g., are friends in the social network) have been spending together, automatically moving the contact suggestions with whom the first user is most likely to interact with to a higher position on the user interface 1100 based on the determined amount of time the users have been spending together.

Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein.

Although an overview of the inventive subject matter has been described with reference to specific example embodiments, various modifications and changes may be made to these embodiments without departing from the broader scope of embodiments of the present disclosure.

The embodiments illustrated herein are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. The Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.

As used herein, the term “or” may be construed in either an inclusive or exclusive sense. Moreover, plural instances may be provided for resources, operations, or structures described herein as a single instance. Additionally, boundaries between various resources, operations, modules, engines, and data stores are somewhat arbitrary, and particular operations are illustrated in a context of specific illustrative configurations. Other allocations of functionality are envisioned and may fall within a scope of various embodiments of the present disclosure. In general, structures and functionality presented as separate resources in the example configurations may be implemented as a combined structure or resource. Similarly, structures and functionality presented as a single resource may be implemented as separate resources. These and other variations, modifications, additions, and improvements fall within a scope of embodiments of the present disclosure as represented by the appended claims. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.

“Signal Medium” refers to any intangible medium that is capable of storing, encoding, or carrying the instructions for execution by a machine and includes digital or analog communications signals or other intangible media to facilitate communication of software or data. The term “signal medium” shall be taken to include any form of a modulated data signal, carrier wave, and so forth. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a matter as to encode information in the signal. The terms “transmission medium” and “signal medium” mean the same thing and may be used interchangeably in this disclosure.

“Communication Network” refers to one or more portions of a network that may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), the Internet, a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, a network or a portion of a network may include a wireless or cellular network and the coupling may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or other types of cellular or wireless coupling. In this example, the coupling may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined by various standard-setting organizations, other long-range protocols, or other data transfer technology.

“Processor” refers to any circuit or virtual circuit (a physical circuit emulated by logic executing on an actual processor) that manipulates data values according to control signals (e.g., “commands”, “op codes”, “machine code”, etc.) and which produces corresponding output signals that are applied to operate a machine. A processor may, for example, be a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC) or any combination thereof. A processor may further be a multi-core processor having two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously.

“Machine-Storage Medium” refers to a single or multiple storage devices and/or media (e.g., a centralized or distributed database, and/or associated caches and servers) that store executable instructions, routines and/or data. The term shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to processors. Specific examples of machine-storage media, computer-storage media and/or device-storage media include non-volatile memory, including by way of example semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), FPGA, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks The terms “machine-storage medium,” “device-storage medium,” “computer-storage medium” mean the same thing and may be used interchangeably in this disclosure. The terms “machine-storage media,” “computer-storage media,” and “device-storage media” specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term “signal medium.”

“Component” refers to a device, physical entity, or logic having boundaries defined by function or subroutine calls, branch points, APIs, or other technologies that provide for the partitioning or modularization of particular processing or control functions. Components may be combined via their interfaces with other components to carry out a machine process. A component may be a packaged functional hardware unit designed for use with other components and a part of a program that usually performs a particular function of related functions. Components may constitute either software components (e.g., code embodied on a machine-readable medium) or hardware components. A “hardware component” is a tangible unit capable of performing certain operations and may be configured or arranged in a certain physical manner. In various example embodiments, one or more computer systems (e.g., a standalone computer system, a client computer system, or a server computer system) or one or more hardware components of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware component that operates to perform certain operations as described herein. A hardware component may also be implemented mechanically, electronically, or any suitable combination thereof. For example, a hardware component may include dedicated circuitry or logic that is permanently configured to perform certain operations. A hardware component may be a special-purpose processor, such as a field-programmable gate array (FPGA) or an application specific integrated circuit (ASIC). A hardware component may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. For example, a hardware component may include software executed by a general-purpose processor or other programmable processor. Once configured by such software, hardware components become specific machines (or specific components of a machine) uniquely tailored to perform the configured functions and are no longer general-purpose processors. It will be appreciated that the decision to implement a hardware component mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software), may be driven by cost and time considerations. Accordingly, the phrase “hardware component” (or “hardware-implemented component”) should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. Considering embodiments in which hardware components are temporarily configured (e.g., programmed), each of the hardware components need not be configured or instantiated at any one instance in time. For example, where a hardware component comprises a general-purpose processor configured by software to become a special-purpose processor, the general-purpose processor may be configured as respectively different special-purpose processors (e.g., comprising different hardware components) at different times. Software accordingly configures a particular processor or processors, for example, to constitute a particular hardware component at one instance of time and to constitute a different hardware component at a different instance of time. Hardware components can provide information to, and receive information from, other hardware components. Accordingly, the described hardware components may be regarded as being communicatively coupled. Where multiple hardware components exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) between or among two or more of the hardware components. In embodiments in which multiple hardware components are configured or instantiated at different times, communications between such hardware components may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware components have access. For example, one hardware component may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware component may then, at a later time, access the memory device to retrieve and process the stored output. Hardware components may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information). The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented components that operate to perform one or more operations or functions described herein. As used herein, “processor-implemented component” refers to a hardware component implemented using one or more processors. Similarly, the methods described herein may be at least partially processor-implemented, with a particular processor or processors being an example of hardware. For example, at least some of the operations of a method may be performed by one or more processors 1004 or processor-implemented components. Moreover, the one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an API). The performance of certain of the operations may be distributed among the processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processors or processor-implemented components may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the processors or processor-implemented components may be distributed across a number of geographic locations.

“Carrier Signal” refers to any intangible medium that is capable of storing, encoding, or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible media to facilitate communication of such instructions. Instructions may be transmitted or received over a network using a transmission medium via a network interface device.

“Computer-Readable Medium” refers to both machine-storage media and transmission media. Thus, the terms include both storage devices/media and carrier waves/modulated data signals. The terms “machine-readable medium,” “computer-readable medium” and “device-readable medium” mean the same thing and may be used interchangeably in this disclosure.

“Client Device” refers to any machine that interfaces to a communications network to obtain resources from one or more server systems or other client devices. A client device may be, but is not limited to, a mobile phone, desktop computer, laptop, portable digital assistants (PDAs), smartphones, tablets, ultrabooks, netbooks, laptops, multi-processor systems, microprocessor-based or programmable consumer electronics, game consoles, set-top boxes, or any other communication device that a user may use to access a network.

“Ephemeral Message” refers to a message that is accessible for a time-limited duration. An ephemeral message may be a text, an image, a video and the like. The access time for the ephemeral message may be set by the message sender. Alternatively, the access time may be a default setting or a setting specified by the recipient. Regardless of the setting technique, the message is transitory. 

What is claimed is:
 1. A method comprising: accessing, from a database coupled to a server computer, user data of a plurality of users; generating, using one or more processors of the server computer, a directed graph of the plurality of users, wherein a weight of a weighted edge between two users of the plurality of users is based, at least partially, on an amount of time the two users have spent together, and computing, by the one or more processors, a first score for each user of the plurality of users, the first score of each user being computed using an edge-weighted edge-based ranking algorithm based on the directed graph, wherein, for each user u of the plurality of users, the first score is expressed as ${{P{R(u)}} = {\sum\limits_{v \in B_{u}}{{W\left( {u,v} \right)}\frac{P{R(v)}}{L(v)}}}},$ wherein a contribution to a value PR(u) of the user u conferred by the weighted edge from a user v to the user u is equal to a value PR(v) of user v divided by a number of edges L(v) from the user v weighted by the weight of the weighted edge W(u, v) from user v to user u, and wherein the value PR(u) for the user u is dependent on the values PR(v) for each user v contained in a set B_(u) including all users linked to user u, divided by the number L(v) of links from the user v, and weighted by the weight W(u, v) of the link from the user v to the user u with respect to the directed graph; computing, by the one or more processors, using a machine learning model, a second score for each user of the plurality of users, the second score of each user being, at least partially, based on the first score of the user; generating, by the one or more processors, a ranked contact suggestion list of one or more users of the plurality of users, the one or more users being ranked based on their respective second score, and the one more users for presenting as contact suggestions to a first user of the plurality of users; and causing display, on a client device of the first user, of the ranked contact suggestion list.
 2. The method of claim 1, further comprising training the machine learning model on a training set comprising a plurality of training examples, the training examples comprising historical contact suggestions and whether the historical contact suggestions resulted in connection requests, and on a learning task defined as predicting a probability of an exemplary user sending a connection request to another exemplary user; and wherein computing the second score for each user of the plurality of users comprises computing, using the trained machine learning model, a probability of the first user sending a connection request to the user.
 3. The method of claim 2, wherein the training examples further comprise whether the historical contact suggestions resulted in connections; and wherein the learning task is further defined as predicting a probability the exemplary user accepting an invitation request received from the other exemplary user; and wherein computing the second score for each user of the plurality of users further comprises computing, using the trained machine learning model, a probability of the user accepting a connection request received from the first user.
 4. The method of claim 1, wherein the one or more users of the plurality of users are the users having a second score that exceeds a threshold.
 5. The method of claim 1, wherein causing display of the ranked contact suggestion list comprises causing display of a user interface comprising a plurality of icons, each icon being associated with one user of the one or more users of the ranked contact suggestion list, a location of each icon on the user interface being based on the second score of the user associated with the icon.
 6. The method of claim 5, further comprising, in response to detecting a user interaction with one icon of the plurality of icons, generating a connection request from the first user to the user associated with the one icon.
 7. The method of claim 1, further comprising: accessing an address book stored in a client device of each user of the plurality of users, wherein the weight of the weighted edge from the first user to a second user of the plurality of users is based on whether the second user is included in the address book of the first user.
 8. The method of claim 1, wherein the weight of the weighted edge from the first user to a second user of the plurality of users is based on whether the first user sent a connection request to the second user.
 9. The method of claim 1, further comprising: tracking a location of at least one user of the plurality of users; and dynamically updating the ranked contact suggestion list as new location data of the at least one user is received.
 10. The method of claim 9, wherein dynamically updating the ranked contact suggestion list comprises: detecting that the first user and a second user of the plurality of users have been spending more time together since the amount of time the first and second users have spent together was computed; computing an updated amount of time the first and second users have spent together; computing, based on the updated amount of time the first and second users have spent together, an updated weight for the weighted edge between the first and second users; generating, based on the updated weight, an updated directed graph of the plurality of users; and computing, based on the updated directed graph, an updated first score for at least one user of the plurality of users; computing, based on the updated first score of the at least one user, a second score for the at least one user; and generating, based on the updated second score of the at least one user, an updated ranked list.
 11. A system comprising: one or more processors; and a memory storing instructions that, when executed by the one or more processors, configure the system to perform operations comprising: accessing user data of a plurality of users; generating a directed graph of the plurality of users, wherein a weight of a weighted edge between two users of the plurality of users is based, at least partially, on an amount of time the two users have spent together; computing a first score for each user of the plurality of users, the first score of each user being computed using an edge-weighted edge-based ranking algorithm based on the directed graph, wherein, for each user u of the plurality of users, the first score is expressed as ${{P{R(u)}} = {\sum\limits_{v \in B_{u}}{{W\left( {u,v} \right)}\frac{P{R(v)}}{L(v)}}}},$ wherein a contribution to a value PR(u) of the user u conferred by the weighted edge from a user v to the user u is equal to a value PR(v) of user v divided by a number of edges L(v) from the user v weighted by the weight of the weighted edge W(u, v) from user v to user u, and wherein the value PR (u) for the user u is dependent on the values PR(v) for each user v contained in a set B_(u) including all users linked to user u, divided by the number L(v) of links from the user v, and weighted by the weight W(u, v) of the link from the user v to the user u with respect to the directed graph; computing, using a machine learning model, a second score for each user of the plurality of users, the second score of each user being, at least partially, based on the first score of the user; generating a ranked contact suggestion list of one or more users of the plurality of users, the one or more users being ranked based on their respective second score, and the one more users for presenting as contact suggestions to a first user of the plurality of users; and causing display, on a client device of a first user of the plurality of users, of the ranked contact suggestion list.
 12. The system of claim 11, the operations further comprising training the machine learning model on a training set comprising a plurality of training examples, the training examples comprising historical contact suggestions and whether the historical contact suggestions resulted in connection requests, and on a learning task defined as predicting a probability of an exemplary user sending a connection request to another exemplary user; and wherein computing the second score for each user of the plurality of users comprises computing, using the trained machine learning model, a probability of the first user sending a connection request to the user.
 13. The system of claim 12, wherein the training examples further comprise whether the historical contact suggestions resulted in connections; and wherein the learning task is further defined as predicting a probability the exemplary user accepting an invitation request received from the other exemplary user; and wherein computing the second score for each user of the plurality of users further comprises computing, using the trained machine learning model, a probability of the user accepting a connection request received from the first user.
 14. The system of claim 11, wherein the one or more users of the plurality of users are the users, among the plurality of users, having a second score that exceeds a threshold.
 15. The system of claim 11, wherein causing display of the ranked contact suggestion list comprises causing display of a user interface comprising a plurality of icons, each icon being associated with one user of the one or more users of the ranked contact suggestion list, a location of each icon on the user interface being based on the second score of the user associated with the icon.
 16. The system of claim 15, the operations further comprising, in response to detecting a user interaction with one icon of the plurality of icons, generating a connection request from the first user to the user associated with the one icon.
 17. The system of claim 11, further comprising: tracking a location of at least one user of the plurality of users; and dynamically updating the ranked contact suggestion list as new location data of the at least one user is received.
 18. The system of claim 17, wherein dynamically updating the ranked contact suggestion list comprises: detecting that the first user and a second user of the plurality of users have been spending more time together since the amount of time the first and second users have spent together was computed; computing an updated amount of time the first and second users have spent together; computing, based on the updated amount of time the first and second users have spent together, an updated weight for the weighted edge between the first and second users; generating, based on the updated weight, an updated user graph of the plurality of users; and computing, based on the updated user graph, an updated first score for at least one user of the plurality of users; computing, based on the updated first score of the at least one user, a second score for the at least one user; and generating, based on the updated second score of the at least one user, an updated ranked list.
 19. A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to perform operations comprising: accessing user data of a plurality of users; generating a directed graph of the plurality of users, wherein a weight of a weighted edge between two users of the plurality of users is based, at least partially, on an amount of time the two users have spent together; computing a first score for each user of the plurality of users, the first score of each user being computed using an edge-weighted edge-based ranking algorithm based on the directed graph, wherein, for each user u of the plurality of users, the first score is expressed as ${{P{R(u)}} = {\sum\limits_{v \in B_{u}}{{W\left( {u,v} \right)}\frac{P{R(v)}}{L(v)}}}},$ wherein a contribution to a value PR (u) of the user u conferred by the weighted edge from a user v to the user u is equal to a value PR(v) of user v divided by a number of edges L(v) from the user v weighted by the weight of the weighted edge W(u, v) from user v to user u, and wherein the value PR (u) for the user u is dependent on the values PR (v) for each user v contained in a set B_(u) including all users linked to user u, divided by the number L (v) of links from the user v, and weighted by the weight W(u, v) of the link from the user v to the user u with respect to the directed graph; computing, using a machine learning model, a second score for each user of the plurality of users, the second score of each user being, at least partially, based on the first score of the user; generating a ranked contact suggestion list of one or more users of the plurality of users, the one or more users being ranked based on their respective second score, and the one more users for presenting as contact suggestions to a first user of the plurality of users; and causing display, on a client device of a first user of the plurality of users, of the ranked contact suggestion list. 